Initial Design For Utilizing Machine Learning In Identifying Diseases In Palm Oil Plant

This research aims to identify diseases in oil palm plants using the Convolutional Neural Network (CNN) method. Oil palm is an essential crop in the plantation industry, but it is susceptible to disease attacks that can harm production results. It is hoped that using technology such as CNN in image pattern recognition can help farmers detect disease quickly and accurately. The CNN method is used to classify images of oil palm leaves based on disease symptoms. The dataset used in this research consists of images of oil palm leaves covering various levels of disease severity. The CNN architecture is structured and trained using training data, then tested using test data that the model has never seen before. Experimental results show that the CNN method can identify diseases in oil palm plants with a significant level of accuracy. This success provides potential for developing a system that can be implemented in the field to support farmers in implementing disease prevention and control measures more efficiently. This research contributes to the development of artificial intelligence-based agricultural technology to support the sustainability of palm oil production. With a fast and accurate disease identification system, it is hoped that it can reduce the negative impact of disease on oil palm plants and increase overall agricultural productivity.